Expected cost/runReporting suggests Gemini 4 Argon may be a larger model and 'could' be pricier to run, implying higher operating costs, but this is presented as expectation rather than confirmed pricing information.
GOOGLE · MODEL RELEASE TRACKER
Gemini 4 Argon
Gemini 4 Argon is reported as an announced Google 'Pro' model in the Gemini family. Media coverage describes it as a forthcoming larger/pro model that could bring notable image-quality improvements and may be more expensive to run, but specifics and confirmed benchmarks were not provided in the excerpts.CURRENT SNAPSHOT4/5 DIMENSIONS WITH DATA
The dimensions that change the decision.
Context window / token capabilityArgon is described as built with an industry‑leading one‑million‑token context capability (the reporting also states support for up to one million output tokens).
Not established from the available sources.
Benchmark reportingMedia reporting cites Google claims that Argon scored higher than competing frontier models (including OpenAI’s GPT‑6 Astra and Anthropic’s Fable/Opus) on a variety of AI benchmarks and is listed as leading on at least one model index cited by Google.
Initial availability / rollout approachArgon is being rolled out in a phased approach, initially to a set of trusted cyber defenders through Google’s Fairwind Program; Google says it is gathering feedback from early testers before expanding availability to developers, enterprises, and consumers.
RELEASE TIMELINE
Published, source-backed release events only.
Google releases Nano Banana 2.1 image model and halves generation prices
Google released Nano Banana 2.1, an image-generation and editing model running on Gemini 3.6 Flash that the company says improves visual quality, text rendering, character consistency, wide panoramas, and infographics while supporting up to 14 reference images and keeping up to four characters and ten objects consistent. Google also cut Nano Banana pricing roughly in half (for example, 1K images from 6.70¢ to 3.36¢), is rolling 2.1 out across products including the Gemini app, AI Mode in Google Search, Google AI Studio, Flow and Stitch, and will shut down the prior gemini-3.1-flash-image model on October 29, 2026.
Google DeepMind unveils Gemini 4 Argon, a frontier model with 1M-token context
Google DeepMind announced Gemini 4 Argon, a new frontier multimodal model optimized for long-horizon reasoning and complex workflows. Argon is rolling out initially to trusted cyber defenders via the Fairwind Program, expands context length to 1 million tokens, reports leading benchmark performance across coding, finance, legal, and video understanding, and will be made more widely available after phased safety testing and engagement with U.S. government pre-release processes; Google also published introductory pricing.
VERIFIABLE FACTS
Every value stays attached to a source and date.
RELEASE · AnnouncementDEVELOPER CLAIM
Reported as Google’s announced next 'Pro' model in the Gemini lineup, referred to as Gemini 4 Argon (announcement mentioned in media excerpts).
AVAILABILITY · Initial availability / rollout approachDEVELOPER CLAIM
Argon is being rolled out in a phased approach, initially to a set of trusted cyber defenders through Google’s Fairwind Program; Google says it is gathering feedback from early testers before expanding availability to developers, enterprises, and consumers.
CAPABILITIES · Targeted capabilities / domainsDEVELOPER CLAIM
Google describes Argon as built to sustain deep reasoning across complex, long‑horizon workflows and delivering frontier performance in real‑world software engineering, enterprise knowledge work (legal, finance), and cybersecurity defense.
PRICING · Expected cost/runDEVELOPER CLAIM
Reporting suggests Gemini 4 Argon may be a larger model and 'could' be pricier to run, implying higher operating costs, but this is presented as expectation rather than confirmed pricing information.
CAPABILITIES · Quantum algorithm optimization exampleDEVELOPER CLAIM
Google reports Argon helped optimize spacetime resources (qubits × gates) for quantum subroutines and in one example beat the published baseline by 40% in minutes.
CAPABILITIES · Memory efficiency / fleet optimizationDEVELOPER CLAIM
Google says Argon agents analyzed fleet‑wide profiling telemetry to identify and apply memory optimizations that freed over 300 TiB once rolled out, with an estimated 500 TiB to 1 PiB in total savings.
CAPABILITIES · Large‑scale code migrationsDEVELOPER CLAIM
Google reports Argon agents are being used for large‑scale codebase migrations and optimizations (e.g., migrating C/C++ codebases to Rust across Google, scaling up to 800K+ lines for the Fuchsia Zircon kernel).
SAFETY · Safety / pre‑release controlsDEVELOPER CLAIM
Google/DeepMind say they are taking a phased release and strengthening 'critical frontier safeguards' before broader rollout; they are participating in the U.S. government’s voluntary pre‑release model access process and plan to iterate on guardrails to defend against misuse (including prompt injection) and monitor for misalignment.
CONTEXT WINDOW · Context window / token capabilityDEVELOPER CLAIM
Argon is described as built with an industry‑leading one‑million‑token context capability (the reporting also states support for up to one million output tokens).
CAPABILITIES · Core capabilities / target workloadsDEVELOPER CLAIM
Argon is engineered for advanced/deep reasoning across complex, long‑horizon workflows and is positioned to excel at heavy‑duty workloads such as coding, cybersecurity defense, enterprise knowledge work (legal, finance), and autonomous cybersecurity patching.
CAPABILITIES · Autonomous cybersecurity capabilityDEVELOPER CLAIM
Reporting states Argon was trained and described as able to autonomously find, validate, and patch critical software vulnerabilities (used particularly for defensive cyber work).
BENCHMARKS · Benchmark reportingDEVELOPER CLAIM
Media reporting cites Google claims that Argon scored higher than competing frontier models (including OpenAI’s GPT‑6 Astra and Anthropic’s Fable/Opus) on a variety of AI benchmarks and is listed as leading on at least one model index cited by Google.
CAPABILITIES · Image qualityDEVELOPER CLAIM
Media reporting states that the next major gain in image quality 'could' come with Gemini 4 Argon — phrasing indicates a potential improvement but not a confirmed capability or benchmark in the provided excerpts.
WHAT CHANGED
Stored passport versions, without reconstructed history.
RELEASE · Announcement+ Reported as Google’s announced next 'Pro' model in the Gemini lineup, referred to as Gemini 4 Argon (announcement mentioned in media excerpts).
RELEASE · Announcement / model type− Google/DeepMind announced Gemini 4 Argon as a new frontier model.
PRICING · Expected cost/run+ Reporting suggests Gemini 4 Argon may be a larger model and 'could' be pricier to run, implying higher operating costs, but this is presented as expectation rather than confirmed pricing information.
CAPABILITIES · Image quality+ Media reporting states that the next major gain in image quality 'could' come with Gemini 4 Argon — phrasing indicates a potential improvement but not a confirmed capability or benchmark in the provided excerpts.
PRICING · Introductory pricing− DeepMind stated introductory pricing: $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at a 95% discount off the input token price.
CAPABILITIES · Core capabilities / target workloads+ Argon is engineered for advanced/deep reasoning across complex, long‑horizon workflows and is positioned to excel at heavy‑duty workloads such as coding, cybersecurity defense, enterprise knowledge work (legal, finance), and autonomous cybersecurity patching.
RELEASE · Announcement / model type+ Google/DeepMind announced Gemini 4 Argon as a new frontier model.
CAPABILITIES · Autonomous cybersecurity capability+ Reporting states Argon was trained and described as able to autonomously find, validate, and patch critical software vulnerabilities (used particularly for defensive cyber work).
AVAILABILITY · Initial availability− Argon is rolling out to a set of trusted cyber defenders through the Fairwind Program; Google says it is using a phased approach and is engaged in the U.S. government's voluntary pre-release access process while gradually expanding access.
BENCHMARKS · Benchmark reporting+ Media reporting cites Google claims that Argon scored higher than competing frontier models (including OpenAI’s GPT‑6 Astra and Anthropic’s Fable/Opus) on a variety of AI benchmarks and is listed as leading on at least one model index cited by Google.
CONTEXT WINDOW · Context window / token capability+ Argon is described as built with an industry‑leading one‑million‑token context capability (the reporting also states support for up to one million output tokens).
PRICING · Introductory pricing+ DeepMind stated introductory pricing: $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at a 95% discount off the input token price.
AVAILABILITY · Initial availability / rollout approach+ Argon is being rolled out in a phased approach, initially to a set of trusted cyber defenders through Google’s Fairwind Program; Google says it is gathering feedback from early testers before expanding availability to developers, enterprises, and consumers.
SAFETY · Safety / pre‑release controls+ Google/DeepMind say they are taking a phased release and strengthening 'critical frontier safeguards' before broader rollout; they are participating in the U.S. government’s voluntary pre‑release model access process and plan to iterate on guardrails to defend against misuse (including prompt injection) and monitor for misalignment.
PRICING · Introductory pricing− Argon will launch at an introductory price of $2 per million input tokens and $10 per million output tokens; cached input tokens are priced at 95% off the input token price.
RELEASE · Announcement− Google/DeepMind announced Gemini 4 Argon as a new "frontier" model on 2026-09-30.
SAFETY · Safety and rollout safeguards− Google says it is taking a phased approach to safely release Argon, gathering feedback from early testers and strengthening frontier safeguards (defenses against misuse and prompt injection, monitoring for misalignment) before broader rollout.